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    <br/>第十五章_元基模拟染色体新陈代谢催化编码
    <br/> 作者: 罗瑶光, Author:Yaoguang.Luo<br/>
    <br/>

    <br/>研究心得

    <br/>
    <br/>最近研发肽化版本产生几个心得总结下:
    <br/>
    <br/>1 养疗经和华瑞集的肽化观测最后是一个文件夹体系. 这个体系全部由文件夹组成,
    文件夹的名字是元基组成.
    <br/>
    <br/>2 说明 养疗经和华瑞集有对应的肽结构, 这个结构我首次命名为类人的智慧永生肽或者生命肽.
    <br/>
    <br/>---20210426上面这个心得可以进行人造染色体和归纳元基染色体, 并提供了真实环境的理论科学依据..
    <br/>
    <br/>我之前的元基分类 研究排上用场了. 关于元基:
    <br/>
    <br/>2年前, 我认为元基是 一个催化计算的 初始单元和基础. 我当时命名为initon - init aton
    for
    calculation
    <br/>
    <br/>1年多前开始DNA的元基编码, 我当时庆幸 元基能解释我的索引单元, 我认为是语义的编码索引最小单元.
    <br/>
    <br/>后来, 我开始DNA的元基计算和肽展公式推导应用, 我觉得元基是 对应(基因元 嘌呤嘧啶)基元的
    语义变换单元.
    <br/>
    <br/>现在随着养疗经的深入, 我开始系统的命名, 元基-是智慧生命进行逻辑表达的最小单元和基础成分.
    <br/>
    <br/>关于催化计算, 我在微信中2021-05-19-12. 07分描述了一句如下:
    <br/>
    <br/>将某一类未解的问题现象和集合, 通过已知的已经健全的学术体系去映射模拟计算,
    最终结果量化并探索能得到未知的答案, 属于一种无理级计算模式.
    <br/>
    <br/>关于肽花的计算, 我在微信中2021-0518描述如下:
    <br/>
    <br/>数据肽索引化后的花状植株, 可不是药用这么简单, 我刚思考了下, 这种蒲公英丝状元基组织是软件的孢子.
    一种全新的数据类软件生命体表达形式,
    <br/>
    <br/>也是永生的必经之路. 我按这种方式进行肽索引, 软件函数分类越来越均匀规则.
    这是一种前沿性智慧分类扩展技术.
    <br/>
    <br/>这个数据生命是以人类的DNA和思维方式为参照设计的. 如果用其他方式构造元基,
    就一定能模拟其他物种, 需要严谨的论证.
    <br/>
    <br/>先别想那么远, 把描述文件弄丰富点.

    <br/>
    <br/>2021年4月26日我在微信说过 华瑞集和养疗经 肽化后的花 就是一种永生药, 现在发现,
    永生根本就不需要到那个巨的级别,
    小小的某一类永生花孢子 就能实现.
    <br/>
    <br/>我要做的不是永生探讨, 这是医学任务, 我要做的是实现孢子软件的自我繁殖, 我定义为
    生命ETL插件节点.
    我的目标是软件的SDLC的自我维护.
    <br/>
    <br/>今天不写代码, 写点思想, 描述我是怎么走到这一步的. 为了确定我的研发计划是否出现跳跃和分歧.
    完善下 UML.
    <br/>
    <br/>2021年5月25日下午16. 36我在微信笔记说过, 均匀化元基索引最后的结果就是均匀化染色体分类,
    呈X状, 并且
    呈蒲公英球状丝化肽元基索引文件夹结构.
    <br/>
    <br/>Java文件可以逐渐的元基文件夹化. 形成文件夹花植株.
    <br/>
    <br/>具体可以参考 如下文件
    <br/>
    <br/>M_UnicornNeroThemeETL\OSI\OPE\SI\MCI\OEI\OVU\PQE\extOSGI\
    OSI_OSU_ASQ_OCQ_OSI_PCI_PCU_MCI_MCU_MSI_OSI_PCI.tvm
    <br/>
    <br/>20210530 我得到一个信息:随着工程的日益发展演化, P元基的类会越来越多,
    如果不进行提取和索引自我AOM元基转化,
    便会产生智慧级(AOPM)元基分类不均匀, 这种倾斜会产生各种问题. --罗瑶光
    <br/>
    <br/>这是我一个动力, 因为O元基和A元基的函数比例越来越小, 在均匀染色体索引的领域,
    我迫切需要做点什么.
    <br/>
    <br/>20210610 在github gitee 留言中描述下: 我做了一个简单的应用操作, 将 包中的相同 元基组
    进行过滤并
    变成文件夹名中. 我定义为 元基的 根枝扩展. (Initons Root Extension IRE)
    <br/>
    <br/>IRE 是 未来数据分类的 一个产业趋势. 作用 分类精简, 数据代谢, 数据生长,
    <br/>
    <br/>我猜测生物的生长过程也是这样的. 共用一种模式.
    <br/>
    <br/>今天开始我把 代码中的analyzer 进行整体替换 为A, 我定义下规则 为 1 函数名 A, 2
    定义域名 _A, 3
    固定名的后缀 _A. 关于2和3的区别通过观察测前缀是否为开头即可.
    <br/>
    <br/>例子如下:
    <br/>
    <br/>A 函数
    <br/>
    <br/>_A 变量
    <br/>
    <br/>A_A 函数
    <br/>
    <br/> 202106132341今天重定义下 元基词汇编码规则. 比如已经有的词汇 suggest/.. . OPE.
    SIU.. .,
    我定义为. OPESIU. 和 _OPESIU_, 变量为 _开头接OPESIU
    <br/>
    <br/>这样以后词汇就好区分了, 同时, 词汇可以进行更迅捷的肽展变换.
    <br/>
    <br/>随着包的越来越小, 现在 函数集合大小仅仅几十兆, 说明元基索引价值越来越明确.
    减少内存占比, 提高计算效率, 缩短寻址时间,
    算能提高越来越可观.
    <br/>
    <br/>索引后进行元基催化计算是一个数字生命诞生的标志. 我迫切需要做些什么, 先把 均匀索引
    实现.
    <br/>
    <br/>20210628 这几天在不断的索引均匀和 染色体模拟, 结合我在英特尔当后端测试的一些工作经验,
    我发现了一些规律.
    <br/>
    <br/>1 sonar lint的 if, while, 等关键字的 嵌套问题化简, 与我现在的新陈代谢的大文件裁剪
    都属于
    数据进化的几种模式.
    <br/>
    <br/>2 元基染色体组均匀索引后, 相同的相似的执行逻辑在同一区间, 非常方便 以后的 元基芯片
    设计 和 软件工程调度.
    提高计算密度. 节约算能
    <br/>
    <br/>3 我找到了一些元基索引规律如 三元基 对称索引, 非对称索引, 移位索引, 反向索引,
    主元基索引. 5种模式.
    <br/>
    <br/>4 关于TXH 的 计算元基 索引, 我会分出一个RNA 包工程来分开处理. 目前这24组双链 为DNA
    包.
    <br/>
    <br/>20210629 今天在裁剪monitor的文件时候, 我把2000多行的源码拆成几个300行的文件,
    运行也不错. 我在思考.
    函数像sonar 那样把嵌套if while 拆分, 是一种危险的
    <br/>
    <br/>执行方法. 正确的思路应该是
    <br/>
    <br/>1 将大文件裁剪成小文件.
    <br/>
    <br/>2 小文件将多个函数拆成多个文件.
    <br/>
    <br/>3 一个函数一个文件还复杂, 应该进行变量全局化重复提取到1, 2 环节.
    <br/>
    <br/>4 如果出现嵌套 if和 while, 不是复杂的计算逻辑, sonar 强行语法分析报错 价值不大.
    当年我写分词就没按sonar来. 思考了下, 当时按照sonar模式搞, 之后我的分词肽化会出问题.
    <br/>
    <br/>
    <br/>
    <br/>5 想起当年 为了搞个 sonar 测试覆盖率非要搞个100%, 这是一个浪费大量时间的弊端.
    <br/>
    <br/>6 最后 sonar的规范中有价值的地方也有, 如 每行的宽度 限制 我不多广告了,
    sonar是普通开源软件,
    任何人都能下得到.
    <br/>
    <br/>20210630 今天在裁剪monitor 模拟新陈代谢, 我把思想规则 浓缩了下. 变成方法如下.
    <br/>
    <br/>1 首先将裁剪的文件名 加 函数名用_XCDX_ 隔开, 如 APP. .. _XCDX_. .. 函数名. java
    <br/>
    <br/>2 隔开后生成的文件中如果有变量变成未知, 则进行全局化, 引用过来.
    <br/>
    <br/>3 全局化的变量再进行 duplication, 之后变成单例类.
    <br/>
    <br/>4 单例类之后变成接口 进行 implement.
    <br/>
    <br/>5 然后整体进行纠正, 将XCDX函数类变成一个 进化插件植株. 这就是二次新陈代谢模拟.
    <br/>
    <br/>20210701 今天在处理数据库的裁剪按20210630的方案, 我有些笔记如下.
    <br/>
    <br/>1 一个文件按一类函数的裁剪价值巨大. 方便以后操作系统级别的 调度肽化.
    <br/>
    <br/>2 逐步的脱离养疗经华瑞集医学属性, 24组元基组 满足API的数据工程适用化, 像java一样,
    接口引用, 参与计算.
    <br/>
    <br/>3 我准备设计一种local static 技术, 满足全局静态. 因为DNA 不同于 RNA,
    我不希望它new来new去.
    不但损耗算能, 还损耗内存.
    <br/>
    <br/>4 这个local static 技术, 我之后会在S_AOPM 里面用.. . SME.. . 元基组下设计研发.

    <br/>
    <br/>Experience of Development about PDN metabolism. 2021-04-26-
    <br/>
    <br/>An observation of the optimizational result, could be like a
    files category system, about document names. And an observation of
    these names was made by sixteen meta-based Initons. The author
    considered his YangLiaoJing and HuaRuiJi had their own PDN structures.
    These PDN structures might be reflected to PDN life and eternal
    intelligence. The conception provides grounds of argument, about
    chromosomes of meta-based Initons for Its software applications.
    <br/>
    <br/>Two years ago, the author considered meta-based Initons was an
    initial unit, means Initon - init aton for calculation. Last year, the
    author considered meta-based Initons could explain well for the indexed
    unit about document names, means Initon - init aton for DNA indexing.
    Then the DNA encoding and PDN extension and Its software applications,
    the author considered meta-based Initons could swap to meta-based
    speeches and gene units. The author considered meta-based Initon were a
    foundational unit of the humanoid life. Then continued lots of proofs
    on them.

    <br/>
    <br/>Experience of Development about PDN Catalyst. 2021-05-19-12:07
    <br/>
    <br/>The author considered an unforeseeable, imcomprehensible and
    computational level, which made definitions and collections of unveiled
    problems, to reflect and simulate a calculation by developed theories
    and desciplines.

    <br/>
    <br/>Experience of Development about PDN Dandelion. 2021-05-18-
    <br/>
    <br/>The author considered a PDN Dandelion of document names, in the
    software domain, could be the software spore, means a blooming data
    life. The author will continue a lot of proofs on them. For example,
    balancing the document names. Meant a balanced PDE, a fashionable
    metabolic classify.

    <br/>
    <br/>Experience of Development about encode of metabolic PDN
    Dandelion. 2021-04-26~2021-5-25-16:36
    <br/>
    <br/>Assume the category of Java file name is
    M_UnicornNeroThemeETL\OSI\OPE\SI\MCI\OEI\OVU\PQE\extOSGI\
    OSI_OSU_ASQ_OCQ_OSI_PCI_PCU_MCI_MCU_MSI_OSI_PCI.
    Tvm. The author got a proof that an increment of P, and a decrement of
    O and A, in the software of file-name Encoding, needed a man-made
    Initon-PDE-swap for P->AOM balancing. 2021-05-30

    <br/>
    <br/>Experience of Development about Initons Root Extension, IRE
    <br/>
    <br/>The author considered an operation about to filter the same
    Initons of function name, added the filtered Initons into a post tail
    of file name. The author considered IRE is a trending logic. It will be
    used in a domain of data classify, data metabolism and data catalyst.
    The author considered an example of word of 'analyzer' will instead of
    upper char of 'A', then could make three definitions: 1 functional
    class 'A', 2 definitional value'_A', and 3 postfix of string target
    like '_A'. The instance as below. (A, a functional class or key value.
    _A, a value. XA_A, a functional string).
    <br/>
    <br/>For example 'A _A = new A(); _A.A_A();' -> 'Analyzer analyzer =
    new Analyzer(), analyzer.XanalyzerAnalyzer();' X could be everything of
    string target.

    <br/>
    <br/>Experience of Development about DNA encode dictionary, for
    example 'suggest/.. . OPE. SIU.. .', the author definited It as
    '.OPESIU.' and '_OPESIU_', It looks be understood easily by humen. And
    the human lexicon could translate to PDN Initons agilely.
    <br/>
    <br/>The author did more proofs about the Initon indexing and Its
    contributions, was more and more obviously. For example, raise up the
    energetic computing effections, and decrement of DMA (direct memory
    access). The indexed Initons catalyst could be a birth of digital life.
    <br/>
    <br/>Experience of Development about Sonar lint at Folsom Intel,
    2021-06-28
    <br/>
    <br/>1 The author considered the sonar lint and Its simplification of
    'if, while and inner loops', was belong to a data evolutional mode,
    also the author's big metabolic file-trim too.
    <br/>
    <br/>2 The author considered Sonar lint means a formated and balanced
    text theory, means the text and code looks simply and well.... Also,
    here the Meta based chromosome which did a balanced index, then made a
    balanced classify. Seem to be a fundamental RNA IC computings and the
    energetic optimization, for the software procedures and computational
    densites.
    <br/>
    <br/>3 Then the author considered five modes of symmetric, asymmetic,
    shift, inverse and PCA indexed Initons.
    <br/>
    <br/>4 Then the author will arrange of TXH, and new F there four
    initons, into a new RNA package. And now the DNA chromosomes classify
    were 24 components.

    <br/>
    <br/>Experience of Development about metabolisms, see the PDN
    metabolisms and secondary metabolisms 2021-06-29, 2021-06-30
    <br/>
    <br/>Experience of Development about local static,
    2021-06-30~2021-07-01
    <br/>
    <br/>For the PDN procedures, the author could use local statics to
    contrst between DNA and RNA computing, because he did not want to new a
    frequent value of DNA. It might be waste of energetic effection. And
    these technologies recently done well in the Initons-dandelion under
    the category file of '.SME.' of '.S_AOPM'. The author would like these
    24 chromosomes, Initons-dandelion, which would be a static interface of
    API. For the seamlessly integrated in extensional computings.
    <br/>
    <br/>The author YaoguangLuo. 稍后优化语法.

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